Precision agriculture is a technique that detects specific attributes of a field and delivers precise amounts of agrochemicals based on these attributes. The field attributes may include the presence of bare spots, weeds, diseased crops, etc. The agrochemicals may include fertilizers, herbicide, insecticide, fungicide, etc. Using precision agriculture, a custom-tailored amount of agrochemical is delivered to each specific area of the field such as herbicide on weeds only, insecticide and fungicide on foliage, and fertilizer in plant areas only. Precision agriculture is a substantial improvement over traditional fanning techniques of evenly spraying the entire crop, irrespective of crop and field variations, which is almost invariably wasteful and expensive.
For example, in the specific case of wild blueberry fields, weeds are the major yield-limiting factor. Weed flora in blueberry fields traditionally consist of slow-spreading perennial species whereas many of the new species invading blueberry fields are common annual weeds of arable fields that produce large number of seeds and require control with herbicides both in prune and production year. Traditionally, herbicides are applied uniformly in wild blueberry fields, but weeds are not distributed uniformly within fields. Moreover wild blueberry fields have significant bare spots (30-50% of total field area). In these situations, spatial information management systems hold great potential for allowing producers to fine-tune the locations, timings, and rates of herbicide application.
Many researchers have attempted to develop variable rate (VR) technologies for various crops although to date little attention has been paid to wild blueberry production systems. Existing VR sprayers deliver pesticides based on prescription maps, developed in GIS software, using aerial spectral scans of wild blueberry fields. However, the GIS-based system was found to be too sensitive to positional error caused by Global Positioning System (GPS) and obtaining up-to-date aerial photography was expensive, the quality was quite variable, and data processing for weed detection was also intensive and difficult.
Ultrasonic sensors have been used for quantification of plant heights. Ultrasonic systems can detect weeds (when they are substantially taller or shorter than the plants) and bare spots in real-time within wild blueberry fields during growing season. Although the ultrasonic systems performed well to detect tall weeds (taller than plants) and bare spots in wild blueberry fields, one serious problem with this technique is that growers apply herbicides during the growing season when the grasses and weeds are not tall enough to sense using ultrasonic sensors, e.g. in April and October.
Spectroscopy techniques have also been explored as disclosed, for example, in U.S. Pat. No. 7,099,004 (Masten). The Masten technique involves collecting and wirelessly transmitting spectral information of a plant for remote analysis.
Machine vision techniques have also been employed for detecting weeds. However, these machine vision systems, based on morphological or textural weed detection methods, generally require a high image resolution and furthermore employ detection algorithms that are very complicated and computationally expensive. These existing machine vision techniques cannot be utilized practically in a mobile spraying system because the time between image capture and spraying is too short to enable the algorithm to identify the crop or field condition.
What is needed therefore is a very efficient machine vision algorithm that can identify the underlying crop or field condition so as to control the sprayer within the short amount of time between image capture and spraying while the mobile sprayer system advances at a normal ground speed.